Our primary practice is litigation support — bringing technical depth and proprietary AI to the discovery process in major litigation. We combine that with purpose-built AI products and business process reengineering to serve organizations across demanding environments.
Our primary practice. Rule 26 Technical Advisory and Litigation Document Intelligence — bringing the technical depth and proprietary AI plaintiff's counsel needs to compete at the highest level of complex litigation.
The Crivella platform — unconventional review, grounded AI infrastructure, context collections, and agentic monitoring — deployed in demanding environments where accuracy and defensibility are non-negotiable.
Technology only delivers lasting value when the processes around it are redesigned to use it well. We lead the organizational change — roles, workflows, governance — that makes AI adoption durable.
The most consequential decisions in major litigation are made before a single deposition is taken. We are there — with the technical depth the other side has always had.
A critical document or piece of evidence that is overlooked or hidden never comes into legal consideration. For all practical purposes, it might as well not exist.
Large corporate defendants arrive at the meet and confer table with a structural advantage. Their records are held across sophisticated enterprise systems built to manage the normal conduct of business at scale — ERP platforms, document management systems, communication archives, operational databases, and proprietary tools that have evolved over years or decades. The people who built and manage those systems understand them in detail. Defense counsel is advised by them. Plaintiff's counsel typically is not.
The asymmetry is twofold: unfamiliarity with where and how business records are created and maintained in the normal course of operations, compounded by unfamiliarity with the computing systems that hold them. Together, these gaps create a dangerous condition — one in which uninformed counsel is taken advantage of and their clients' interests are compromised before a single deposition is taken or a single motion is filed.
Under Rule 26(f), parties are required to meet and confer and develop a written discovery plan governing the scope, form, and sequence of document production. Under Rule 26(b)(1), that scope is bounded by relevance and proportionality. Both provisions are routinely used by well-advised defense counsel to narrow what enters the case. Without technical counterweight, agreements that appear reasonable on their face can silently eliminate entire categories of critical evidence.
Crivella provides that counterweight. We work alongside plaintiff's counsel at every stage of the Rule 26 process — before negotiations begin, at the table, and through the enforcement of agreements after they are made.
Data Landscape Mapping
Before negotiations begin, we build a complete picture of the opposing party's data environment — custodians, enterprise systems, databases, third-party platforms, and archived environments — so counsel knows what should be in scope before the other side defines it.
Custodian & Non-Custodial Source Intelligence
We identify who holds what — including non-custodial locations such as shared systems, operational databases, and repositories that individual custodian productions would never surface and that adversaries have no incentive to volunteer.
Spoliation Detection
We assess whether documents and data are being destroyed, migrated, or moved in ways designed to place them beyond the reach of discovery. Evidence that disappears before it is identified never comes into play.
Linguistic & Terminology Analysis
Every large organization has its own internal language — terminology, abbreviations, and operational shorthand used in the normal course of business. We analyze that language to ensure search terms and culling agreements do not silently exclude critical evidence under neutral-sounding language.
Proportionality & Scope Defense
Proportionality arguments under Rule 26(b)(1) are a standard tool for narrowing production. We ensure that scope-reduction decisions are made with full visibility into what is at stake — and that no critical document is excluded under the guise of burden reduction.
Production Sequencing & Compliance
We map the relationships between people and processes within the opposing organization and determine the optimal sequence for custodian and non-custodial productions — ensuring deposition preparation is conducted with the fullest possible knowledge. After agreements are made, we monitor compliance and identify gaps and patterns that indicate production obligations are not being honored.
Context Collection Construction
Collections assembled around specific legal theories, custodians, timelines, or factual issues — structured so AI works within a precisely bounded, relevant scope.
AI-Powered Document Review
Grounded AI identifies and annotates documents relevant to the specific legal theory — with citations to the source document in the collection, not inferences drawn from training data.
Deposition & Motion Preparation
Rapid identification of key documents for specific uses — deposition preparation, expert support, motion drafting — drawn from the full production and annotated with citations.
Production Gap Analysis
AI-driven analysis of production contents against what the data landscape map says should be present — surfacing gaps, anomalies, and patterns that indicate incomplete or non-compliant production.
When production arrives, most plaintiff firms face the same problem: volume that exceeds human capacity, and no reliable method to find what matters before it is needed.
Conventional document review — even with TAR, email threading, and keyword search — was built for defense counsel's production obligation: process everything, produce what is responsive, and protect what is privileged. That is a breadth problem. Plaintiff's counsel has a precision problem: find the documents that prove the specific facts this case requires, and find them before the deadline that matters.
Crivella's Litigation Document Intelligence service applies unconventional review to your production. Legal theory drives the construction of context collections. Grounded AI works within those collections to identify and annotate with citations. A small expert team validates and acts. The output is not a document review report — it is targeted, defensible intelligence, structured for immediate use in motion practice, depositions, and expert preparation.
Every finding is traceable to a specific document. Every citation is verifiable. Every output is defensible under adversarial scrutiny.
Before any technology is deployed, we establish the process and governance foundation it will operate on.
Most AI projects fail in the planning stage — not because the technology isn't ready, but because organizations don't understand what they're actually asking AI to operate on. Before any implementation begins, we map the reality of how your organization works.
We examine your existing workflows, decision-making processes, data environments, and risk exposures. We identify where AI can create genuine value, where it cannot, and — critically — what needs to change in your processes before AI can be effective.
The output is a clear-eyed assessment: what's possible, what it requires, and what a responsible path forward looks like.
Workflow Mapping
Documenting how work actually gets done — not how it's supposed to get done.
Data Environment Review
Assessing the quality, structure, and governance of the data AI will rely on.
Risk & Decision Analysis
Identifying where AI influences consequential decisions and what controls need to be in place.
Readiness Report & Roadmap
A plain-language assessment with specific, prioritized recommendations for moving forward.
Process Redesign
Rebuilding workflows from the ground up around what AI makes possible — not patching the old ones.
Role & Responsibility Realignment
Defining how human judgment and AI capability divide the work — clearly and defensibly.
Change Management
Supporting the people side of adoption — training, communication, and the transition to new ways of working.
Quality & Productivity Benchmarking
Establishing the baselines against which real improvements will be measured.
Technology adoption fails when it asks people to work in new ways without redesigning how those ways actually function. We address that directly.
Process reengineering for AI adoption means more than writing new procedures. It means rethinking how decisions get made, how information flows, how work is reviewed, and how accountability is maintained when AI is part of the picture.
We work with your people to design the new operating model — one that uses AI where it creates value, keeps human judgment where it matters, and builds in the controls that make the whole thing defensible.
With the processes right, we deploy AI that is purpose-built for high-stakes environments — and built to last.
Two inventions — separated by five years — together constitute the complete architecture of what the AI industry now calls enterprise knowledge management and retrieval-augmented generation. The first, invented by Arthur Ray Crivella in 2001, built the knowledge repository: ingestion, organization, and access at scale. The second, invented in 2006, built the content identification engine: the patented methodology for scoring, curating, and bounding collections of relevant content before feeding them to AI systems. Together, they solve the fundamental problem of AI accuracy: garbage in, garbage out — at the data curation layer, before the model ever sees the input. Every major platform vendor is building toward what Crivella invented and has been applying in world-class situations for twenty-five years.
The Crivella platform is the foundation for unconventional review — the methodology that replaces exhaustive document processing with goal-driven, AI-powered, collection-grounded precision. The goal is not to review documents. The goal is to prove facts.
Context collections are assembled around a legal theory, a custodian, a timeline, or a factual issue. AI working within a precisely bounded collection produces output that is traceable, verifiable, and defensible — grounded AI, not hallucinating AI. Every output is anchored to a specific document in a specific collection.
Human professionals and AI systems work from the same repository, simultaneously. Work product flows back in — analyses, annotations, deposition preparation, motion support — compounding the platform's value as each engagement progresses. Agentic monitoring maintains continuous awareness of what is changing in the production environment without waiting to be asked.
The result is a small, well-informed team — supported by twenty-five years of platform development and two foundational patents — that operates with a level of precision and speed that no conventional review model can match.
Explore the Platform →Goal-driven document intelligence — structured around proving facts, not processing documents. Legal theory drives collection construction. AI identifies and annotates. A small expert team validates and acts.
A patented methodology (invented 2006) that curates precisely bounded document collections around legal theories, custodians, and timelines — structured as grounded AI inputs that eliminate hallucination at the data layer.
Secure, process-aware knowledge repository AI systems access directly — outputs are traceable to specific documents, verifiable against the collection, and defensible in adversarial review.
Production monitoring, alert, and reporting agents that maintain continuous awareness of complex data environments — surfacing changes, gaps, and anomalies without waiting to be asked.
With the process work done, implementation becomes significantly more straightforward — and significantly more likely to deliver what was promised.
We implement AI solutions built around your reengineered processes, not the other way around. That means the technology fits your actual workflows, your actual data, and the actual decisions your people need to make.
We work across document intelligence, workflow automation, decision support, large language model applications, and custom integrations — selecting and deploying what fits the problem, not what's fashionable.
Document Intelligence
AI-powered extraction, review, classification, and analysis of documents at scale.
Workflow Automation
Automating repetitive, rule-based tasks so your people can focus on the work that requires judgment.
Decision Support Systems
AI that informs and accelerates decisions — with the human oversight frameworks that keep those decisions defensible.
LLM Applications & Integration
Deploying large language model capabilities — research, drafting, summarization, Q&A — within controlled, governed environments.
Data Governance Frameworks
Policies, controls, and access structures that protect the integrity and confidentiality of the data AI uses.
AI Decision Oversight
Human-in-the-loop frameworks that ensure consequential AI outputs are reviewed, validated, and traceable.
Risk & Liability Controls
Governance structures designed to protect your organization in regulated, high-stakes, or litigation-sensitive environments.
Audit Trails & Accountability
Documentation and logging frameworks that make AI-assisted decisions defensible — to clients, regulators, and courts.
AI is only as trustworthy as the controls around it. In environments where data is sensitive, decisions are consequential, and accountability is non-negotiable — governance isn't a checkbox. It's the foundation.
We build governance frameworks that protect your data, ensure oversight of AI-influenced decisions, and manage the risk and liability exposure that AI adoption creates.
For law firms in particular, this work is essential: privilege, confidentiality, and the quality of legal advice cannot be compromised by poorly governed AI. We design the controls that make AI adoption safe in your environment.
Assessment through implementation and governance — the complete transformation engagement for organizations ready to commit to lasting change.
Scoped to a specific service area — assessment only, process reengineering only, or governance framework development — for organizations with defined, targeted needs.
Retained advisory relationships for organizations that want ongoing guidance as their AI capabilities evolve and their operational environment changes.
Every engagement begins with a conversation. Tell us where you are, where you want to go, and what's in the way. We'll take it from there.
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